Knowledge graphs include nodes representing entities and edges running between nodes that represent interactions or relations between the nodes. For example, a knowledge graph may include a node for the person “Albert Einstein”, a node for the paper “The Foundation of the General Theory of Relativity”, and an edge representing authorship runs between the two nodes, indicating that Albert Einstein has the relationship of author for the paper on General Relativity. A given node may contain several aliases for the entity that it represents (e.g., “Albert Einstein” and “A. Einstein”) that enable a search engine or query to locate a given node by various terms. Aliases are clustered at various nodes based on the contexts from which they are learned, but as new entities are discovered, maintaining proper alias-to-entity relations becomes increasingly difficult, and mistakes are made and corrected in successive classifications; resulting in churn in relationships in the knowledge graph. For example, another “A. Einstein” (e.g., Amy, Alexander, Alicia, etc., Einstein) may be tracked in the knowledge graph, and over successive constructions of the knowledge graph the two Einsteins may be treated as the same or separate entities and the related entities to one Einstein may be associated erroneously with the other; degrading the functionality of search engines using the knowledge graph and wasting computing resources.